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The development of semiconducting polymers with good processability in green solvents and competitive electrical performance is essential for realizing sustainable large-scale manufacturing and commercialization of organic electronics. A major obstacle is the processability-performance dichotomy that is dictated by the lack of ideal building blocks with balanced polarity, solubility, electronic structures, and molecular conformation. Herein, through the integration of donor, quinoid and acceptor units, an unprecedented building block, namely TQBT, is introduced for constructing a serial of conjugated polymers. The TQBT, distinct in non-symmetric structure and high dipole moment, imparts enhanced solubility in anisole-a green solvent-to the polymer TQBT-T. Furthermore, PTQBT-T possess a highly rigid and planar backbone owing to the nearly coplanar geometry and quinoidal nature of TQBT, resulting in strong aggregation in solution and localized aggregates in film. Remarkably, PTQBT-T films spuncast from anisole exhibit a hole mobility of 2.30 cm2 V-1 s-1, which is record high for green solvent-processable semiconducting polymers via spin-coating, together with commendable operational and storage stability. The hybrid building block emerges as a pioneering electroactive unit, shedding light on future design strategies in high-performance semiconducting polymers compatible with green processing and marking a significant stride towards ecofriendly organic electronics.

Redox-switchable polymerizations of lactide and epoxides were extended to the solid state by anchoring an iron-based polymerization catalyst to TiO2 nanoparticles. The reactivity of the molecular complexes and their redox-switching characteristics were maintained in the solid-state. These properties resulted in surface-initiated polymerization reactions that produced polymer brushes whose chemical composition is dictated by the oxidation state of the iron-based complex. Depositing the catalyst-functionalized TiO2 nanoparticles on fluorine-doped tin oxide resulted in an electrically addressable surface that could be used to demonstrate spatial control in redox-switchable polymerization reactions. By using a substrate that contained two electrically isolated domains wherein one domain was exposed to an oxidizing potential, patterns of surface-bound polyesters and polyethers were accessible through sequential application of lactide and cyclohexene oxide. The differentially functionalized surfaces demonstrated distinct physical properties that illustrated the promise for using the method to pattern surfaces with multiple, chemically distinct polymer brushes.

Redox-switchable polymerizations of lactide and epoxides were extended to the solid state by anchoring an iron-based polymerization catalyst to TiO2 nanoparticles. The reactivity of the molecular complexes and their redox-switching characteristics were maintained in the solid-state. These properties resulted in surface-initiated polymerization reactions that produced polymer brushes whose chemical composition is dictated by the oxidation state of the iron-based complex. Depositing the catalyst-functionalized TiO2 nanoparticles on fluorine-doped tin oxide resulted in an electrically addressable surface that could be used to demonstrate spatial control in redox-switchable polymerization reactions. By using a substrate that contained two electrically isolated domains wherein one domain was exposed to an oxidizing potential, patterns of surface-bound polyesters and polyethers were accessible through sequential application of lactide and cyclohexene oxide. The differentially functionalized surfaces demonstrated distinct physical properties that illustrated the promise for using the method to pattern surfaces with multiple, chemically distinct polymer brushes.

Image deblurring aims to restore the detailed texture information or structures from blurry images, which has become an indispensable step in many computer vision tasks. Although various methods have been proposed to deal with the image deblurring problem, most of them treated the blurry image as a whole and neglected the characteristics of different image frequencies. In this paper, we present a new method called multi-scale frequency separation network (MSFS-Net) for image deblurring. MSFS-Net introduces the frequency separation module (FSM) into an encoder-decoder network architecture to capture the low- and high-frequency information of image at multiple scales. Then, a cycle-consistency strategy and a contrastive learning module (CLM) are respectively designed to retain the low-frequency information and recover the high-frequency information during deblurring. At last, the features of different scales are fused by a cross-scale feature fusion module (CSFFM). Extensive experiments on benchmark datasets show that the proposed network achieves state-of-the-art performance., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Milgram empirically showed that people knowing only connections to their friends could locate any person in the U.S. in a few steps. Later research showed that social network topology enables a node aware of its full routing to find an arbitrary target in even fewer steps. Yet, the success of people in forwarding efficiently knowing only personal connections is still not fully explained. To study this problem, we emulate it on a real location-based social network, Gowalla. It provides explicit information about friends and temporal locations of each user useful for studies of human mobility. Here, we use it to conduct a massive computational experiment to establish new necessary and sufficient conditions for achieving social search efficiency. The results demonstrate that only the distribution of friendship edges and the partial knowledge of friends of friends are essential and sufficient for the efficiency of social search. Surprisingly, the efficiency of the search using the original distribution of friendship edges is not dependent on how the nodes are distributed into space. Moreover, the effect of using a limited knowledge that each node possesses about friends of its friends is strongly nonlinear. We show that gains of such use grow statistically significantly only when this knowledge is limited to a small fraction of friends of friends.

Lithium metal anode holds great promises for next-generation battery technologies but is notoriously difficult to work with. The key to solving this challenge is believed to lie in the ability of forming stable solid-electrolyte interphase (SEI) layers. To further address potential safety issues, it is critical to achieve this goal in nonflammable electrolytes. Building upon previous successes in forming stable SEI in conventional carbonate-based electrolytes, here we report that reversible Li stripping/plating could be realized in triethyl phosphate (TEP), a known flame retardant. The critical enabling factor of our approach was the introduction of oxygen, which upon electrochemical reduction induces the initial decomposition of TEP and produces Li3 PO4 and poly-phosphates. Importantly, the reaction was self-limiting, and the resulting material regulated Li plating by limiting dendrite formation. In effect, we obtained a functional SEI on Li metal in a nonflammable electrolyte. When tested in a symmetric Li∥Li cell, more than 300 cycles of stripping/plating were measured at a current density of 0.5 mA cm-2 . Prototypical Li-O2 and Li-ion batteries were also fabricated and tested to further support the effectiveness of this strategy. The mechanism by which the SEI forms was studied by density functional theory (DFT), and the predictions were corroborated by the successful detection of the intermediates and products.

Under-display cameras have been proposed in recent years as a way to reduce the form factor of mobile devices while maximizing the screen area. Unfortunately, placing the camera behind the screen results in significant image distortions, including loss of contrast, blur, noise, color shift, scattering artifacts, and reduced light sensitivity. In this paper, we propose an image-restoration pipeline that is ISP-agnostic, i.e. it can be combined with any legacy ISP to produce a final image that matches the appearance of regular cameras using the same ISP. This is achieved with a deep learning approach that performs a RAW-to-RAW image restoration. To obtain large quantities of real under-display camera training data with sufficient contrast and scene diversity, we furthermore develop a data capture method utilizing an HDR monitor, as well as a data augmentation method to generate suitable HDR content. The monitor data is supplemented with real-world data that has less scene diversity but allows us to achieve fine detail recovery without being limited by the monitor resolution. Together, this approach successfully restores color and contrast as well as image detail.

Simultaneous reconstruction of geometry and reflectance properties in uncontrolled environments remains a challenging problem. In this paper, we propose an efficient method to reconstruct the scene's 3D geometry and reflectance from multi-view photography using conventional hand-held cameras. Our method automatically builds a virtual scene in a differentiable rendering system that roughly matches the real world's scene parameters, optimized by minimizing photometric objectives alternatingly and stochastically. With the optimal scene parameters evaluated, photo-realistic novel views for various viewing angles and distances can then be generated by our approach. We present the results of captured scenes with complex geometry and various reflection types. Our method also shows superior performance compared to state-of-the-art alternatives in novel view synthesis visually and quantitatively.

Recently, deep learning based image deblurring has been well developed. However, exploiting the detailed image features in a deep learning framework always requires a mass of parameters, which inevitably makes the network suffer from high computational burden. To solve this problem, we propose a lightweight multiinformation fusion network (LMFN) for image deblurring. The proposed LMFN is designed as an encoder-decoder architecture. In the encoding stage, the image feature is reduced to various smallscale spaces for multi-scale information extraction and fusion without a large amount of information loss. Then, a distillation network is used in the decoding stage, which allows the network benefit the most from residual learning while remaining sufficiently lightweight. Meanwhile, an information fusion strategy between distillation modules and feature channels is also carried out by attention mechanism. Through fusing different information in the proposed approach, our network can achieve state-of-the-art image deblurring result with smaller number of parameters and outperforms existing methods in model complexity.

Treatment recommendations within Clinical Practice Guidelines (CPGs) are largely based on findings from clinical trials and case studies, referred to here as research studies, that are often based on highly selective clinical populations, referred to here as study cohorts. When medical practitioners apply CPG recommendations, they need to understand how well their patient population matches the characteristics of those in the study cohort, and thus are confronted with the challenges of locating the study cohort information and making an analytic comparison. To address these challenges, we develop an ontology-enabled prototype system, which exposes the population descriptions in research studies in a declarative manner, with the ultimate goal of allowing medical practitioners to better understand the applicability and generalizability of treatment recommendations. We build a Study Cohort Ontology (SCO) to encode the vocabulary of study population descriptions, that are often reported in the first table in the published work, thus they are often referred to as Table 1. We leverage the well-used Semanticscience Integrated Ontology (SIO) for defining property associations between classes. Further, we model the key components of Table 1s, i.e., collections of study subjects, subject characteristics, and statistical measures in RDF knowledge graphs. We design scenarios for medical practitioners to perform population analysis, and generate cohort similarity visualizations to determine the applicability of a study population to the clinical population of interest. Our semantic approach to make study populations visible, by standardized representations of Table 1s, allows users to quickly derive clinically relevant inferences about study populations., Comment: 16 pages, 4 figures, 1 table, accepted to the ISWC 2019 Resources Track (https://iswc2019.semanticweb.org/call-for-resources-track-papers/)

Milgram empirically showed that people knowing only connections to their friends could locate any person in the U.S. in a few steps. Later research showed that social network topology enables a node aware of its full routing to find an arbitrary target in even fewer steps. Yet, the success of people in forwarding efficiently knowing only personal connections is still not fully explained. To study this problem, we emulate it on a real location-based social network, Gowalla. It provides explicit information about friends and temporal locations of each user useful for studies of human mobility. Here, we use it to conduct a massive computational experiment to establish new necessary and sufficient conditions for achieving social search efficiency. The results demonstrate that only the distribution of friendship edges and the partial knowledge of friends of friends are essential and sufficient for the efficiency of social search. Surprisingly, the efficiency of the search using the original distribution of friendship edges is not dependent on how the nodes are distributed into space. Moreover, the effect of using a limited knowledge that each node possesses about friends of its friends is strongly nonlinear. We show that gains of such use grow statistically significantly only when this knowledge is limited to a small fraction of friends of friends.

Development of the Li-O2 battery into a practical technology hinges on the availability of a stable electrolyte. Because of the high reactivity of oxygen species in the system, no known organic electrolytes meet the stability requirements. The search for a suitable electrolyte system remains an outstanding challenge in Li-O2 battery research. Here, we show that the issue can be solved with the use of a water-in-salt electrolyte system that involves no organic solvents. In essence, the electrolyte consists of super-concentrated LiTFSI (lithium bis(trifluoromethanesulfonyl)imide), in which H2O molecules are locked to the ions and exhibit little reactivity toward Li2O2 or other oxygen species. The net result is a highly effective electrolyte that permits stable Li-O2 battery operations on the cathode with superior cycle lifetimes. A new door to practical Li-O2 batteries with high performance is opened up. As an electrochemical energy storage technology that holds the highest theoretical energy density, the Li-O2 battery has been studied for over two decades. Its reported performance, however, remains much lower than expected. An important reason for the slow progress is the lack of stable electrolytes. Here, we present a solution to such a challenge. We show that the “water-in-salt� electrolyte, which is essentially a super-concentrated aqueous solution, enables stable operation of a Li-O2 battery on the basis of reversible formation and decomposition of Li2O2 for superior long cycle lifetimes. More importantly, it presents a platform for future optimizations to realize the full potential of the Li-O2 battery as a stable, high-capacity electrochemical energy storage technology. Development of the Li-O2 battery as a practical energy storage technology has been underpinned by the lack of a stable electrolyte to enable reversible conversion between O2 and Li2O2, given that previous applied organic electrolytes all show reactivity toward reactive oxygen species. Wang a

Memristor-based oscillator is becoming promising thanks to its inherent NDR (Negative Differential Region) property and compact circuit structure. This paves the way to the large scale oscillatory neural network (ONN) and the realization of pattern recognition based on its global synchronization. However, the simulation of large scale ONN encounters the problem of long simulation time because of the large number of oscillators. Here we propose a highly efficient method to abstract the phase sensitivity characteristic of the memristor-based oscillator, i.e., its PPV (Perturbation Projection Vector), which allows reducing considerably the complexity of ONN simulation, and speeding up the simulation more than 2000 times. Our study also reveals the impact of the circuit parameters on the pattern recognition accuracy and the robustness against the frequency mismatch.

In this letter, we propose for the first time a method of abstracting the PPV (Perturbation Projection Vector) characteristic of the up-to-date memristor-based oscillators. Inspired from biological oscillators and its characteristic named PRC (Phase Response Curve), we build a bridge between PRC and PPV. This relationship is verified rigorously using the transistor level simulation of Colpitts and ring oscillators, i.e., comparing the PPV converted from PRC and the PPV obtained from accurate PSS+PXF simulation. Then we apply this method to the PPV calculation of the memristor-based oscillator. By keeping the phase dynamics of the oscillator and dropping the details of voltage/current amplitude, the PPV modelling is highly efficient to describe the phase dynamics due to the oscillator coupling, and will be very suitable for the fast simulation of large scale oscillatory neural networks.

Mast cells are known to play an important role in allergic events and in other inflammatory reactions through varied intracellular signaling transduction proteins. RasGRP4 is a mast cell-restricted guanine nucleotide exchange factor (GEF) and diacylglycerol (DAG)/phorbol ester receptor. Interleukin (IL) -13, a critical cytokine for allergic inflammation, exerts its effects through a complex receptor system including IL-4Rα, IL-13Rα1 and IL-13Rα2. IL-13Rα2 has been reported to be a decoy receptor for IL-13. My experiments indicate that the mast cell specific RasGRP4 protein regulates the level of IL 13Rα2 and controls IL-13/ IL 13Rα1-mediated intracellar signaling events in mast cells. Phosphorylation of STAT6 plays an important role in airway hyperresponsiveness and asthma. The development of therapeutics that can regulate RasGRP4 could be used to modulate the IL-13-induced phosphorylation of STAT-6 that may be used as therapy in patients with asthma. SLE is a complex, heterogeneous systemic autoimmune disease characterized by the presence of high levels of autoantibodies. Dysregulation of RasGRP1, a Ras active gene, in mice resulted in a SLE-like disorder. Yasuda and coworkers demonstrated that a defective isoform of RasGRP1 (Δ11) was present in a subset of patients with SLE. My experiments indicate that RasGRP1 upregulates the expression of IL2RG in T cells. In contrast the Δ11 RasGRP1 isoform expressed in a subset of SLE patients leads to defective expression of IL2RG. The IL2RG chain is a common chain which forms part of a number of different receptors eg. IL-2, 4, 7, 9, 15, 21. IL-2 as well as IL-21, which shares sequence homology with IL-2, has been reported to be involved in the generation of regulatory T cells (Tregs). In SLE patients, CD4 + CD25+ Tregs, which play an essential role in controlling immunologic tolerance to self-antigens and preventing autoimmunity, are significantly decreased when compared with healthy